In classification problems, extra consideration has to be given when constructing the training set. The number of samples collected from each class distribution should be comparable to avoid bias. [1]}. As an example, in [2]}, the authors investigated how a balanced training set could impact the accuracy of an ML classifier, predicting the building and floor a user is located at. Balancing the initial dataset improved the localization accuracy by about 1%.
